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PROCESS AND DEVICE FOR THE APPROXIMATE DETERMINATION OF HEARTBEAT TIMES

20220323017 · 2022-10-13

    Inventors

    Cpc classification

    International classification

    Abstract

    A process and a signal processing unit (5) approximately detect a respective characteristic heartbeat time {H_Zp[f](x), H_Zp[s](x.sub.1), . . . , H_Zp[s](x.sub.N)} per heartbeat for a sequence of heartbeats of a patient (P). A sensor array (2.1, 2.2) sends at least one sum signal [Sig.sub.Sum(1), Sig.sub.Sum(2)], which results from a superimposition of a cardiogenic signal and of a respiratory signal. A first detector (25.1) calculates a respective first detection result for each characteristic heartbeat time, and a second detector (25.2, . . . ) calculates a second detection result. The first detector (25.1) analyzes a different sum signal and/or applies a different method of analysis than the second detector (25.2, . . . ). The signal processing unit (5) calculates a respective estimation (representation) for each heartbeat time and uses this estimation as the characteristic heartbeat time. The signal processing unit (5) uses a first detection result and a second detection result to calculate the estimation.

    Claims

    1. A process for representing a respective characteristic heartbeat time per heartbeat for a sequence of heartbeats of a patient, the process comprising the steps of: providing a signal processing unit comprising a first detector and a second detector and a sensor arrangement comprising at least one sensor array configured to measure a variable, which correlates with cardiac activity of the patient and/or with intrinsic breathing activity of the patient; generating at least one sum signal using measured values of the sensor array, wherein the sum signal generated or every generated sum signal comprises a superimposition of a cardiogenic signal and a respiratory signal, wherein the cardiogenic signal correlates with the cardiac activity of the patient and the respiratory signal correlates with an the intrinsic breathing activity of the patient; calculating, with the first detector, a first detection result for the characteristic heartbeat time by analyzing the or one sum signal; calculating, with the second detector, a second detection result for the characteristic heartbeat time by at least one of: analyzing another sum signal that is different from the sum signal analyzed by the first detector; analyzing the sum signal that is analyzed by the first detector and applying a different method of analysis than that applied by the first detector; and analyzing another sum signal that is different from the sum signal analyzed by the first detector and applying a different method of analysis than that applied by the first detector; and with the signal processing unit calculating a representation for the characteristic heartbeat time by using the first detection result and the second detection result.

    2. A process in accordance with claim 1, wherein a cardiogenic signal segment, being a predefined cardiogenic signal segment, is given or the signal processing unit determines the cardiogenic signal segment by using a sample, wherein the cardiogenic signal segment approximately describes a temporal course of the cardiac activity of the patient in a course of a single heartbeat, wherein the sample comprises a plurality of segments of the sum signal or of the other sum signal, wherein each segment of the sample refers to a respective time period, in which a single heartbeat is carried out, and wherein the signal processing unit determines the cardiogenic signal using the detected characteristic heartbeat times and the predefined or determined cardiogenic signal segment.

    3. A process in accordance with claim 1, wherein the sensor arrangement comprises a first sensor array and a second sensor array, wherein the first sensor array comprises at least one first sensor and the second sensor array comprises at least one second sensor, wherein at least one of: the second sensor is arranged at position in relation to the heart that is different from a position of the of the first sensor in relation to the heart; and the second sensor applies a different measuring method than that applied by the first sensor, and wherein the sum signal is a first sum signal that is generated by using measured values of the first sensor array and the other sum signal is a second sum signal that is generated by using measured values of the second sensor array; wherein the first detector calculates the first detection result for each characteristic heartbeat time by analyzing the first sum signal; and wherein the second detector calculates the respective second detection result for each characteristic heartbeat time by analyzing the second sum signal.

    4. A process in accordance with claim 3, wherein the signal processing unit calculates a first representation and a second representation for the respiratory signal; the signal processing unit calculates the first representation for the respiratory signal by using the first sum signal and the respective representation for each characteristic heartbeat time; and the signal processing unit calculates the second representation for the respiratory signal by using the second sum signal and the respective representation for each characteristic heartbeat time.

    5. A process in accordance with claim 1, wherein: the first detector comprises a first real time detector and an additional first detector and the second detector comprises a second real time detector and an additional second detector; a calculation period is predefined, the first real time detector calculates a first real time detection result for the characteristic heartbeat time in the calculation period; the additional first detector calculates an additional first detection result for the characteristic heartbeat time; the second real time detector calculates a second real time detection result for the characteristic heartbeat time in the calculation period; the additional second detector calculates an additional second detection result for the characteristic heartbeat time; the signal processing unit calculates a respective real time representation for each characteristic heartbeat time in the calculation period by using the first real time detection result and the second real time detection result; and the signal processing unit calculates a respective additional representation for each characteristic heartbeat time by using the first additional detection result and the second additional detection result.

    6. A process in accordance with claim 5, wherein: wherein the sum signal is a first sum signal and the other sum signal is a second sum signal; the first sum signal and the second sum signal are generated by using measured values of the sensor arrangement; the second sum signal is generated by using measured values of a different sensor and/or based on a different method for processing measured values than that used to generate the first sum signal; the first real time detector calculates the first real time detection result by using the first sum signal; and the second real time detector calculates the second real time detection result by using the second sum signal.

    7. A process in accordance with claim 5, wherein: the signal processing unit calculates, for N heartbeats, a respective additional representation for the characteristic heartbeat times of the N heartbeats, wherein N>1 and wherein the calculated additional representation for the characteristic heartbeat times has a higher reliability than the calculated representation for each characteristic heartbeat time; the signal processing unit calculates a respective statistical deviation indicator for the deviation between the detection result for the first detector and for the second detector for the characteristic heartbeat time and the respective additional representation for the characteristic heartbeat time, which additional representation is calculated with higher reliability; wherein the signal processing unit calculates the statistical deviation indicator by using the N additional representations; and wherein for the first detector and/or for the second detector, the signal processing unit corrects, by calculation, each additional detection result provided by the first detector and the second detector based on the statistical deviation indicator calculated for the first detector and for the second detector.

    8. A process in accordance with claim 5, wherein: the signal processing unit concludes the calculation of the real time representation before the heartbeat has ended; and the signal processing unit concludes the calculation of the additional representation after the end of the heartbeat.

    9. A process in accordance with claim 1, wherein at least one of the first detector and the second detector calculates a quality indicator comprising an indicator of a reliability that the detection result or results provided by the at least one of the first detector and the second detector coincides with the characteristic heartbeat time; and the signal processing unit calculates the representation for a characteristic heartbeat time by using the detection result or results and the quality indicator.

    10. A process in accordance with claim 9, wherein the signal processing unit additionally calculates a quality indicator for the representation for a characteristic heartbeat time as a function of the quality indicators for the detection results.

    11. A process in accordance with claim 1, wherein: the signal processing unit calculates at least one representation for the respiratory signal by using the sum signal or the other sum signal; the step of calculating the representation for the respiratory signal comprises, with the signal processing unit, compensating by calculation an effect of the cardiogenic signal on the sum signal or on the other sum signal using the detected characteristic heartbeat times.

    12. A signal processing unit for representing a respective characteristic heartbeat time per heartbeat for a sequence of heartbeats of a patient, the signal processing unit comprising: a first detector; and a second detector, wherein the signal processing unit is configured: to receive measured values from a sensor arrangement comprising at least one sensor array, wherein the sensor arrangement is configured to measure a variable, which correlates with at least one of cardiac activity of the patient and intrinsic breathing activity of the patient; and to generate at least one sum signal using received measured values. wherein the sum signal or every sum signal comprises a respective superimposition of a cardiogenic signal and of a respiratory signal, wherein the cardiogenic signal correlates with the cardiac activity of the patient and the respiratory signal correlates with the intrinsic breathing activity of the patient, wherein the first detector is configured to calculate a first detection result for each characteristic heartbeat time by analyzing the sum signal, wherein the second detector is configured to calculate a respective second detection result for the characteristic heartbeat time by at least one of: analyzing another sum signal that is different from the sum signal analyzed by the first detector; analyzing the sum signal that is analyzed by the first detector and applying a different method of analysis than that applied by the first detector; and analyzing another sum signal that is different from the sum signal analyzed by the first detector and applying a different method of analysis than that applied by the first detector, wherein the signal processing unit is configured to calculate a respective representation for the characteristic heartbeat time, and wherein the signal processing unit is configured to calculate the representation of the characteristic heartbeat time by using the first detection result and the second detection result.

    13. A signal processing unit in accordance with claim 12, wherein: the signal processing unit comprises a first real time detector and an additional first detector as the first detector and comprises a second real time detector and an additional second detector as the second detector; the first real time detector is configured to calculate a respective first real time detection result for the characteristic heartbeat time in a predefined calculation period, the second real time detector is configured to calculate a respective second real time detection result for the characteristic heartbeat time in the calculation period, the additional first detector is configured to calculate an additional first detection result for each characteristic heartbeat time, the additional second detector is configured to calculate an additional second detection result for each characteristic heartbeat time, the signal processing unit is configured: to calculate a respective real time representation for the characteristic heartbeat time of a heartbeat in the calculation period by using the first real time detection result and the second real time detection result; and to calculate a respective additional representation for the characteristic heartbeat time by using the first additional detection result and the second additional detection result.

    14. A signal processing unit according to claim 12, in combination with a sensor arrangement comprising at least one sensor array configured: to measure at least one variable, which correlates with the cardiac activity and/or with the patient's own breathing activity, wherein the signal processing unit is configured to receive measured values from the sensor array or each sensor array; and to generate the sum signal or each sum signal by using received measured values.

    15. A system comprising: a ventilator; and an arrangement comprising: a signal processing unit for representing a respective characteristic heartbeat time per heartbeat for a sequence of heartbeats of a patient, the signal processing unit comprising: a first detector; and a second detector, wherein the signal processing unit is configured: to receive measured values from a sensor arrangement comprising at least one sensor array, wherein the sensor arrangement is configured to measure at least one variable, which correlates with at least one of cardiac activity of the patient and intrinsic breathing activity of the patient; and to generate at least one sum signal using received measured values, wherein the sum signal or every sum signal comprises a respective superimposition of a cardiogenic signal and of a respiratory signal, wherein the cardiogenic signal correlates with cardiac activity of the patient and the respiratory signal correlates with the patient's own breathing activity, wherein the first detector is configured to calculate a first detection result for each characteristic heartbeat time by analyzing the sum signal, wherein the second detector is configured to calculate a second detection result for each characteristic heartbeat time by at least one of: analyzing another sum signal that is different from the sum signal analyzed by the first detector; analyzing the sum signal that is analyzed by the first detector and applying a different method of analysis than that applied by the first detector; and analyzing another sum signal that is different from the sum signal analyzed by the first detector and applying a different method of analysis than that applied by the first detector, wherein the signal processing unit is configured to calculate at least one respective representation for each characteristic heartbeat time, and wherein the signal processing unit is configured to calculate the representation or each representation of a characteristic heartbeat time by using at least one respective first detection result and at least one respective second detection result; and the sensor arrangement, wherein the signal processing unit is configured to calculate a representation for the respiratory signal by using the sum signal or the other sum signal, wherein the signal processing unit is configured to compensate an effect of the cardiogenic signal on the used sum signal or the other sum signal by using the detected characteristic heartbeat times, and wherein the ventilator is configured to ventilate a patient including carry out ventilation as a function of the representation for the respiratory signal.

    Description

    BRIEF DESCRIPTION OF THE DRAWINGS

    [0076] In the drawings:

    [0077] FIG. 1 is a view showing an exemplary segment of a cardiogenic signal in the course of a single heartbeat;

    [0078] FIG. 2 is a schematic view showing which sensors/sensor arrays measure which different variables, which are used for the determination of an estimated respiratory signal;

    [0079] FIG. 3 is an exemplary view showing an exemplary course of the sum signal as well as two heartbeat times and two heartbeat time periods;

    [0080] FIG. 4 is a view showing three exemplary courses of a cardiogenic signal;

    [0081] FIG. 5 is a schematic view showing the two function blocks for determining the estimated respiratory signal;

    [0082] FIG. 6 is an exemplary view showing a reference signal segment that is generated and used;

    [0083] FIG. 7 is a view showing an exemplary course of the compensation signal and two exemplary heartbeat time periods;

    [0084] FIG. 8 is an exemplary schematic view showing heart time representations calculated based on five sum signals and a ventilator actuated by means of two sum signals;

    [0085] FIGS. 9 (1/2) and 9(2/2) is an exemplary schematic view showing signals that are combined (merged) from four channels; and

    [0086] FIG. 10 is an exemplary schematic view showing signals from two channels that are combined in real time.

    DESCRIPTION OF PREFERRED EMBODIMENTS

    [0087] In the exemplary embodiment, the present invention is used for the ventilation and/or the automatic analysis of the vital parameters of a patient.

    [0088] “Signal” shall be defined below as the course in the time range or even in the frequency range of a directly or indirectly measurable and temporally variable variable which correlates with a physical variable (a physical quantity that varies). In the present case, this physical variable is connected with the cardiac activity and/or with the intrinsic breathing activity and/or with the other muscle activity of a patient and/or with the ventilation of the patient and is generated by at least one signal source in the body of the patient and/or by a ventilator. A “respiratory signal” correlates with the intrinsic breathing activity of the patient, a “cardiogenic signal” correlates with the cardiac activity of the patient. A segment of this signal which pertains to a defined period is designated below as “signal segment.” The value of a signal at a defined time is designated as a signal value or even as a signal segment value.

    [0089] In the exemplary embodiment, the present invention is used to automatically determine an estimation (representation) Sig.sub.res.est for an electrical respiratory signal Sig.sub.res, wherein the respiratory signal Sig.sub.res to be estimated (represented) correlates with the intrinsic breathing activity of a patient P. This intrinsic breathing activity may be triggered by electrical pulses in the body of the patient, which the patient himself/herself generates, i.e., it may be a spontaneous breathing, or may be stimulated from outside. In both cases, the diaphragm muscles of the patient P accomplish this intrinsic breathing activity. This distinguishes the intrinsic breathing activity from a ventilation, which is caused by ventilation strokes of a ventilator. The subscript est indicates that the signal is estimated (is a representation of the signal).

    [0090] In one application of the exemplary embodiment, the patient P is ventilated mechanically at least from time to time, while the estimated respiratory signal Sig.sub.res.est is determined. The present invention is used in another application to monitor the patient P and to use the respiratory signal Sig.sub.res to be estimated for this purpose, without the patient P being necessarily ventilated mechanically in this case.

    [0091] Both the respiratory signal Sigres and the determined estimation Sig.sub.res.est are variable over time, i.e., Sig.sub.res=Sig.sub.res(t) and Sig.sub.res.est=Sig.sub.res.est(t).

    [0092] This respiratory signal Sig.sub.res cannot be measured directly. On the one hand, the pulses generated in the body of the patient, which “actuate” the respiratory muscles, cannot be measured directly especially when electrodes pick up measured values on the skin of the patient, but only electrical pulses, which are generated during the contraction of the muscle fibers of the respiratory muscles. In addition, the electrical pulses, which cause the intrinsic breathing activity of the patient P, are superimposed by electrical pulses, which cause the cardiac activity of the patient P; more precisely: which are formed during the contraction of the heart muscles. Therefore, after a corresponding processing of measured values, only a sum signal Sig.sub.sum can be measured directly. This sum signal Sig.sub.Sum is formed from a superimposition of the respiratory signal Sig.sub.res being sought, which correlates with the intrinsic breathing activity of the patient, and of a cardiogenic signal Sig.sub.kar, which correlates with the cardiac activity.

    [0093] A typical segment of an electrically measured cardiogenic signal Sig.sub.kar in the course of a single heartbeat is shown in FIG. 1. A reference heartbeat time period H_Zr.sub.ref is shown on the x axis, and the signal value is shown on the y axis, for example, in millivolts. Five peaks P, Q, R, S and T can be seen. The characteristic heartbeat time is, for example, the R peak or the temporal center between the Q peak and the S peak of this heartbeat.

    [0094] The sum signal Sig.sub.Sum is formed from the superimposition of the respiratory signal Sig.sub.res with the cardiogenic signal Sigkar. As a rule, the sum signal Sig.sub.Sum is additionally superimposed by unwanted signals.

    [0095] FIG. 2 schematically shows which signals can be generated from measured values by the measured values being processed automatically in a suitable manner. Shown are [0096] the mechanically ventilated patient P, [0097] the trachea Sp and the diaphragm Zw of the patient P, [0098] a ventilator 1, which mechanically ventilates the patient Pat least from time to time and which comprises a data-processing signal processing unit 5, wherein the signal processing unit 5 has reading access and writing access to a memory 9 at least from time to time, [0099] an intercostal pair 2.1 with two measuring electrodes 2.1.1 and 2.1.2, which are arranged on the right and on the left of the sternum and between two respective ribs of the patient P, i.e., in an area located near the heart, [0100] a pair 2.2 located near the diaphragm with two measuring electrodes 2.2.1 and 2.2.2, which are arranged near the diaphragm of the patient P, [0101] an electrode for ground, not shown, [0102] a pneumatic sensor 3, which is located at a distance in space from the body of the patient P and comprises a measured value transducer, which is arranged, for example, in front of the mouth of the patient P, as well as an analysis unit, which may be arranged in the ventilator 1, [0103] an optional sensor 4, which comprises an image recording device and an image analysis unit and is directed toward the chest area of the patient P, [0104] an optional pneumatic sensor 6 in the form of a probe or a balloon in the trachea Sp and near the diaphragm Zw of the patient P, [0105] a cuff 7 around a wrist of the patient P, wherein this cuff 7 holds a catheter 17 for measuring the time course of the blood pressure invasively, [0106] two finger clips 8.1, 8.2, which are placed over a respective finger of the patient P or are positioned at a different location on the skin of the patient P, wherein the one finger clips 8.1 measures the degree of saturation of the blood with oxygen non-invasively, preferably by means of a plethysmographic method, and the other finger clip 8.2 measures the blood pressure of the patient P non-invasively, and [0107] optionally electrodes, not shown, in the trachea of the patient P.

    [0108] The intercostal pair 2.1 and the ground electrode deliver a first sum signal Sig.sub.Sum[1] after signal processing. The pair 2.2 located near the diaphragm and the ground electrode deliver a second sum signal Sig.sub.Sum[2] after signal processing. The additional sensors described above may deliver additional sum signals Sig.sub.sum[n], n >=3. It is also possible that the same sensor array delivers two different sum signals, for example, by applying different measuring methods. Such a sensor array delivering two different sum signals is described, for example, in DE 10 2009 035 018 A1 (corresponding U.S. US201 1028819 A1 is incorporated herein by reference). “The sum signal Sig.sub.Sum” is discussed briefly below.

    [0109] The signal processing preferably comprises a so-called baseline filtering. This process comprises the step that an average course is determined from the raw signal Sigmw, for example, by means of a statistical method, and the average course is subtracted from the raw signal Sig.sub.raw. A course with isoelectric points is used in one application instead of the average course.

    [0110] For example, a sum signal Sig.sub.Sum in the form of a mechanomyogram (MMG signal) can also be generated and used instead of an electrical signal (EMG signal). Only the EMG sensors or MMG sensors are needed for the exemplary embodiment. It is also possible to generate such a signal as a sum signal Sig.sub.Sum, which signal correlates with the time course of the change in the blood volume in the body of the patient, for example, by means of measured values, which are obtained by optical plethysmography.

    [0111] The optical sensor 4 repeatedly measures a respective value for at least one anthropological parameter of the patient. The parameter is, for example, the current lung filling level and/or the current sitting posture of the patient P. The optical sensor 4 comprises, for example, a camera or other image recording device as well as an image analysis unit.

    [0112] An indicator Pm, for the airway pressure and/or an indicator P. for the tracheal pressure can be generated from the measured values of the other sensors as well as of sensors, not shown, in the interior of the ventilator 1, and a pneumatic indicator Rum, which is likewise an indicator of the intrinsic breathing activity of the patient P, can be derived therefrom. According to a preferred embodiment, an estimation (representation) Sig.sub.res.est for the electrical or mechanical respiratory signal Sig.sub.res, on the one hand, and a pneumatic indicator P.sub.mus, on the other hand, are determined. Thanks to this combination, the intrinsic breathing activity of the patient P is determined with higher reliability than in case of deriving only one signal. Furthermore, thanks to this combination it can in many cases be deduced how well the respiratory muscles of the patient P convert electrical pulses in the body of the patient P into pneumatic breathing activity (neuromechanical efficiency). The present invention can also be used in an embodiment, in which even the EMG signal or the MMG signal is generated, but not the pneumatic indicator P., for the breathing activity.

    [0113] The estimated respiratory signal Sig.sub.res.est determined according to the present invention is used, for example, for the following purposes: [0114] A pneumatic indicator P.sub.mus for the intrinsic breathing activity of the patient P is derived by using the respiratory signal Sig.sub.res.est. The ventilation strokes, which the ventilator 1 brings about, are synchronized as well as possible with the intrinsic breathing activity of the patient P. [0115] The neuromechanical efficiency of the breathing of the patient P is determined. This efficiency is an indicator of how well the respiratory muscles convert electrical signals into spontaneous breathing. [0116] The state of the respiratory muscles of the patient P is determined (fatigue determination); the pneumatic indicator P.sub.mus is not needed for this. [0117] Asynchronies in the intrinsic breathing activity of the patient are detected; the pneumatic indicator Pmus is not needed for this as well. [0118] In order to monitor the patient P, the estimated respiratory signal Sig.sub.res.est and the respiratory EMG performance are determined and outputted as two vital parameters in a form perceptible to a person, preferably visually in the form of a respective time course, optionally together with the airway pressure P.sub.aw or the tracheal pressure P.sub.es. [0119] It is possible that the patient P is not fully sedated, but rather the diaphragm muscles of the patient P carry out an intrinsic breathing activity. In this situation, an assist of the intrinsic breathing activity by a ventilation is triggered and/or carried out as a function of the estimated respiratory signal Sig.sub.res.est. The ventilation strokes of the ventilation are preferably carried out as a function of the estimated (represented) respiratory signal Sig.sub.res.est. For example, the ventilator I triggers the ventilation strokes as a function of the estimated respiratory signal Sig.sub.res.est and/or ends the ventilation strokes and/or sets the respective amplitude of each ventilation stroke and/or the temporally variable frequency of the Is ventilation strokes as a function of the estimated respiratory signal Sig.sub.res.est. The duration and the end of a ventilation stroke may also be automatically regulated as a function of the estimated respiratory signal Sig.sub.res.est.

    [0120] In order to regulate the ventilator 1 during the ventilation of the patient P or to monitor the patient P and to use the estimated respiratory signal Sig.sub.res.est for the regulation or monitoring, the estimated respiratory signal Sig.sub.res.est is determined with a high sampling frequency and/or with a short calculation period, i.e., the signal processing unit 5 delivers a new signal value Sig.sub.res.est(t) at each sampling time t. “High sampling frequency” is defined as an interval of less than 5 [msec], and preferably less than 3 msec located between two consecutive sampling times. “Short calculation time” is defined as a calculation time of less than 5 [msec], preferably less than 3 msec. In particular, the sampling frequency is preferably at least 1 kHz, especially preferably at least 2 kHz for the fatigue determination. Some steps of the process described below are, by contrast, carried out in the exemplary embodiment with a low sampling frequency, namely with a frequency that is in the range of the heartbeat rate, i.e., between 1 Hz and 2 Hz. The calculation time is long in another embodiment.

    [0121] FIG. 3 shows an exemplary time course of a sum signal Sig.sub.Sum[.] The segment shown in FIG. 3 comprises four breaths and a plurality of heartbeats. Shown are four periods Atm(1), . . . , Atm(4) of the four breaths and for the two exemplary heartbeats x and y a respective heartbeat time period H_Zp(x) and H_Zp(y) and a characteristic heartbeat time H_Zp(x) and H_Zp(y). It can be seen that the cardiogenic signal Sig.sub.kar in one heartbeat time period is many times greater than the respiratory signal Sig.sub.res in this period. Outside of the heartbeat time period H_Zr(x), H_Zr(y), the respiratory signal Sig.sub.res is, however, sufficiently strong compared to the cardiogenic signal Sig.sub.kar and may therefore be determined from the sum signal Sig.sub.Sum, for example, by the sum signal Sig.sub.Sum being used as the estimated respiratory signal Sig.sub.res.est outside of each heartbeat time period.

    [0122] FIG. 4 shows in an exemplary manner three typical time courses of the cardiogenic signal Sig.sub.kar, namely from top to bottom [0123] a pneumatic signal P.sub.kar for the ventricular pressure, measured in mmHg, [0124] an electrical ECG signal, measured in millivolts, as well as [0125] an acoustic signal Phkar for the heart sounds, measured with an acoustic sensor.

    [0126] The x axis applies to all three courses. The y axes pertain to the respective mass unit of the signal. The time t is plotted on the x axis; the respective value of the cardiogenic signal Sig.sub.kar is plotted on the y axis. The period shown overlaps two consecutive heartbeats x and y. In case of the ECG signal, each heartbeat comprises a so-called P wave, a QRS phase and a T wave.

    [0127] The respective heartbeat time period H_Zr(x) or H_Zr(y) as well as the characteristic heartbeat times HZp(x) and H_Zp(y) of the two exemplary heartbeats No. x and No. y are shown for each time course. For example, the R peak is used as a characteristic time HZp(x) of a heartbeat in case of the ECG signal. The interval RR between two consecutive heartbeats as well as the QRS amplitude QRS of a heartbeat are shown in FIG. 4. As is suggested in FIG. 4, the cardiogenic signal Sig.sub.kar is three orders of magnitude greater than the respiratory signal Sig.sub.res in the area of the P wave up to the T wave of a heartbeat and equal or smaller in the remaining area. As can, furthermore, be seen in FIG. 4, how and especially with what length a heartbeat time period and a characteristic heartbeat time H_Zp(x), H_Zp(y), . . . are specified depends on the signal used. Different possible specifications are shown in FIG. 4.

    [0128] The sum signal or each sum signal Sig.sub.Sum is a superimposition of the respiratory signal Sig.sub.res being sought and of the cardiogenic signal Sig.sub.kar as well as optionally of unwanted signals. In one application of the present invention, the characteristic heartbeat time H_Zp(x) of each heartbeat x is used to compensate by calculation the effect of the cardiogenic signal Sig.sub.kar on a sum signal Sig.sub.sum.

    [0129] FIG. 5 schematically shows a measured value processor 19. This measured value processor 19 processes the raw signal Sigiaw, which is delivered by the sensors 2.1.1 through 2.2.2 after a signal amplification. The measured value processor 19 eliminates by calculation low-frequency vibrations, standardizes the raw signal Sig.sub.raw, for example, by means of the above-described baseline filtering, and delivers the sum signal Sig.sub.Sum.

    [0130] FIG. 5 schematically shows, furthermore, a function block 20, which receives the sum signal Sig.sub.Sum and carries out the just mentioned compensation by calculation of the cardiogenic signal Sig.sub.kar. This function block 20 carries out different signal processing steps in order to eliminate by calculation the cardiogenic signal Sig.sub.kar from a sum signal Sig.sub.Sum, i.e., to compensate at least partially the effect of the cardiac activity on a measured sum signal Sig.sub.Sum. For each heartbeat x, the function block 20 detects in the sum signal Sig.sub.Sum a sum signal segment SigA.sub.Sum(x), in which the heartbeat x takes place. Each sum signal segment SigA.sub.sum(x) pertains to the same reference heartbeat time period H_Zr.sub.ref, cf. FIG. 1. In the heartbeat time period H_Zr(x), the sum signal Sig.sub.Sum is almost exclusively determined by the cardiogenic signal Sig.sub.kar, so that the remaining signal components can be ignored. The function block 20 delivers a compensation signal Sig.sub.com.

    [0131] A functional unit 11 of the compensation function block 20 generates a synthetic cardiogenic signal Sig.sub.kar.syn, which is an approximation (estimation) for the cardiogenic signal Sig.sub.kar and is composed of signal segments. The function unit 11 compensates by calculation the contribution of the cardiogenic signal Sig.sub.kar to the sum signal Sig.sub.Sum, for example, by subtraction of the synthetic cardiogenic signal Sig.sub.kar.syn, and generates the compensation signal Sig.sub.com as a result.

    [0132] Exemplary methods for generating such a compensation signal Sig.sub.com are described in the following publications, which are incorporated herein by reference: [0133] M. Ungureanu and W. M. Wolf: “Basic Aspects Concerning the Event-Synchronous Interference Canceller,” IEEE Transactions on Biomedical Engineering, Vol. 53, No. 11 (2006), pp. 2240-2247; [0134] L. Kahl and U. G. Hofmann: “Removal of ECG artifacts from EMG signals with different artifact magnitudes by template subtraction,” Current Directions in Biomedical Engineering, 2019; 5(1), pp. 357-360; [0135] DE 10 2007 062 214 B3 (corresponding to U.S. Pat. No. 8,109,269 B2), [0136] EP 3 381 354 A1; and [0137] the subsequently published patent disclosure DE 10 2019 006 866 A1.

    [0138] In an embodiment the compensation function block 20 applies one of the methods described in the above-mentioned publications.

    [0139] The compensation function block 20 generates in an initialization phase Ip a cardiogenic reference signal segment SigA.sub.kar.ref, which is valid for this patient P in this current situation and which is stored in the memory 9, and applies this signal segment again to each heartbeat in a subsequent use phase. The initialization phase Ip is preferably repeated continuously, for which the respective last N heartbeats are used. As a result, the reference signal segment SigA.sub.kar.ref is updated continuously and is especially adapted to an altered state of the patient P. N is preferably between 50 and 100. The value N=9 is used in FIG. 6.

    [0140] The following steps are carried out in both phases Ip, Np: [0141] A functional unit 12 identifies the heartbeat time period H_Zr(x) of each heartbeat x in the sum signal Sig.sub.Sum, preferably the respective beginning, e.g., the P peak, and the respective end, e.g., the T peak, and/or the respective QRS phase of each heartbeat x. [0142] A functional unit 13 determines the respective characteristic heartbeat time H_Zp(x) of each heartbeat, especially preferably with a tolerance of a few msec. The tolerance is especially preferably at most half of the period between two consecutive sampling times for the determination of the sum signal Sig.sub.Sum, wherein this period is preferably below 1 msec. [0143] The function block 22 with the functional units 12 and 13 needs a plurality of values of the sum signal Sig.sub.Sum for a plurality of consecutive sampling times to determine the characteristic heartbeat time H_Zp(x) of each heartbeat with sufficient accuracy. What consequences this has, for example, for the actuation of the ventilator 1 is explained below.

    [0144] Furthermore, the following steps are carried out in the initialization phase Ip: [0145] A sum signal segment SigA.sub.Sum(x) of the sum signal Sig.sub.Sum belongs to each heartbeat No. x, cf. FIG. 3 and FIG. 4. A functional unit 14 superimposes by calculation the N sum signal segments SigA.sub.sum(x.sub.1), . . . , SigA.sub.Sum(x.sub.N) for the last N heartbeats x.sub.1, . . . , x.sub.N. As needed, these sum signal segments SigA.sub.Sum(x.sub.1), . . . , SigA.sub.Sum(x.sub.N) are cut, compressed or stretched to a consistent length. A process for superimposition of segments is described in M. Ungureanu and W. M. Wolf, mentioned above. The N sum signal segments SigA.sub.Sum(x.sub.1), SigA.sub.Sum(x.sub.N) for the N heartbeats are preferably superimposed such that the N sum signal segments have the same length and the R peaks are located above one another. Each sum signal segment SigA.sub.Sum(x) thus pertains to the same reference heartbeat time period H_Zr.sub.ref. A relative time in this reference heartbeat time period H_Zr.sub.ref is designated by τ. A relative time τ=τ(t) in this relative heartbeat time period corresponds to each absolute time t of the sum signal segment SigA.sub.Sum(x). Instead of the designation “relative time,” the designation “heart phase ϕ” with a value range from 0° to 360° or from 0 to 2 π can be used. [0146] A functional unit 15 generates a cardiogenic reference signal segment (template) SigA.sub.kar,ref from the superimposition of N sum signal segments SigA.sub.Sum(x.sub.1), SigA.sub.Sum(x.sub.N), which the functional unit 14 has generated. This cardiogenic reference signal segment SigA.sub.kar.ref describes approximately the course of the cardiogenic signal Sig.sub.kar during a single heartbeat and likewise pertains to the reference heartbeat time period H_Zr.sub.ref. The characteristic heartbeat time H_Zp(x) is preferably τ=0. As was already mentioned, the cardiogenic component in the sum signal Sig.sub.Sum is many times greater than the respiratory component during a heartbeat, and the respiratory components during a heartbeat are largely “averaged out” by averaging over N sum signal segments. The functional unit 15 preferably applies a learning method to the N sum signal segments SigA.sub.Sum(x.sub.1), SigA.sub.Sum(x.sub.N). The cardiogenic reference signal segment SigA.sub.kar.ref is preferably stored in the memory 9.

    [0147] The following steps are carried out in the use phase Np: [0148] The functional unit 13 detects in the sum signal Sig.sub.sum the heartbeats and determines the respective characteristic heartbeat time H_Zp(x) of each heartbeat x detected. [0149] For each heartbeat x, the cardiogenic reference signal segment SigA.sub.kar.ref is used again. In one embodiment, this is subtracted unchanged from the sum signal segment SigA.sub.kar.rer (template subtraction). The functional unit 16 carries out this step. [0150] By contrast, the functional unit 16 optionally additionally uses the value of at least one anthropological parameter, which influences the cardiac activity and thus the cardiogenic signal Sig.sub.kar and has been measured in case of this heartbeat No. x. The lung filling level and an indicator of the current posture of the patient P as well as the interval RR between the R peaks of two consecutive heartbeats are examples of such an anthropological parameter. The functional unit 16 adapts for each heartbeat the cardiogenic reference signal segment SigA.sub.kar,ref to the parameter value or each parameter value measured during this heartbeat x and as a result generates a cardiogenic signal segment SigA.sub.kar.syn(x). [0151] The functional unit 16 positions the cardiogenic reference signal segment SigA.sub.kar.ref or optionally the adapted cardiogenic signal segment SigA.sub.kar.syn(x) of the current heartbeat in a correctly timed manner, e.g., in a QRS-synchronized manner. As a result, a new synchronized segment of the synthetic cardiogenic signal Sig.sub.kar.syn is generated. The synthetic cardiogenic signal Sig.sub.kar.syn is preferably outputted in a form perceptible by a person. [0152] A functional unit 11 compensates in the newest sum signal segment SigA.sub.Sum(x) the effect of the cardiogenic signal Sig.sub.kar, for example, by subtracting the cardiogcnic reference signal segment SigA.sub.kar.ref or the adapted cardiogenic signal segment SigA.sub.kar.syn(x) from the newest sum signal segment SigA.sub.Sum(x).

    [0153] A preferred embodiment for applying a learning method in the initialization phase Ip as well as the respective value of an anthropological parameter in the use phase for each heartbeat is described in the subsequently published patent disclosure DE 10 2019 006 866 A1 mentioned above.

    [0154] As the beginning of the process, i.e., after the patient P is connected to the measuring electrodes 2.1.1 through 2.2.2, the initialization phase Ip is carried out, which comprises a time period of N heartbeats. This initialization phase Ip is preferably carried out again, especially with the respective last N heartbeats. In this initialization phase Ip, the compensation function block 20 generates, as described above, an initial cardiogenic reference signal segment SigAkar.me as a function of the sum signal segments SigA.sub.Sum(x.sub.1), SigA.sub.Sum(x.sub.N) for the last N heartbeats.

    [0155] During the process, i.e., in the use phase Np, the compensation function block 20 adapts the cardiogenic reference signal segment SigA.sub.kar.ref to the respective last N heartbeats, i.e., to the last N sum signal segments SigA.sub.Sum(x.sub.1), SigA.sub.Sum(x.sub.N) and stores the cardiogenic reference signal segment in the memory 9. The steps in the initialization phase Ip and the adaptation to the respective last N heartbeats are carried out with the low sampling frequency, which is approximately equal to the heartbeat rate.

    [0156] The N sum signal segments for a respective heartbeat are preferably superimposed with the double time resolution of the sum signal Sig.sub.Sum. This means: The values of the sum signal Sig.sub.Sum are determined with a high sampling frequency, i.e., the interval Δt between two sampling times is 1/f. The time resolution is increased by calculation to, e.g., 2f or 3f, e.g., by a respective signal value Sig.sub.Sum(t+Δt/2) being positioned by calculation, for example, by interpolation between two signal values Sig.sub.Sum(t) and Sig.sub.Sum(t+Δt) derived from measured values.

    [0157] After the initialization phase Ip, the following steps are carried out with the high sampling frequency (a few msec or even only a few tenths of a msec): [0158] The signal processing unit 5 derives from measured values a respective new value Sig.sub.Sum(t) for the sum signal Sig.sub.Sum. [0159] The function block 22 with the functional units 12 and 13 detects in the sum signal Sig.sub.Sum the beginning and the precise characteristic time H_Zp(x) of a heartbeat x and determines a new sum signal segment (SigA.sub.Sum(x) as a result. [0160] The compensation function block 20 optionally adapts the cardiogenic reference signal segment SigA.sub.kar.ref to the respective value of at least one anthropological parameter, determines the associated relative time τ=τ(t) and generates an additional signal segment by positioning in a correctly timed manner, namely the temporally newest segment SigA.sub.kar.syn(x) of the synthetic cardiogenic signal Sig.sub.kar.syn. [0161] The functional unit 11 subtracts from the new value Sig.sub.Sum(t) the value SigA.sub.kar.ref[τ(t)] or SigA.sub.kar.syn(x)[τ(t)] of the cardiogenic reference signal segment SigA.sub.kar.ref or the value of the adapted cardiogenic signal segment SigA.sub.kar.ref(x) for the same relative time τ, i.e.,


    Sig.sub.com(t)=Sig.sub.Sum(t)−SigA.sub.kar[τ(t)] or


    Sig.sub.com(t)=Sig.sub.Sum(t)−SigA.sub.kar.syn(x)[τ(t)].

    [0162] It is also possible that the functional unit 11 compensates the cardiogenic effect in a different way, for example, by means of a high pass filter or by an independent component analysis or by a so-called blind source separation, which is described, for example, in DE 10 2015 015 296 A1. [0163] The compensation function block 20 outputs a new signal segment SigA.sub.com(x) for the compensation signal Sig.sub.com.

    [0164] The output signal Sig.sub.cum of the compensation function block 20 is used in one embodiment as the estimated signal Sig.sub.res.est for the respiratory signal Sig.sub.res being sought. In another embodiment, the output signal is attenuated, and especially by an attenuation function block 21, cf. FIG. 5. The attenuation function block 21 preferably comprises a high pass filter with a limit frequency between 10 Hz and 50 Hz to remove low-frequency residues of the cardiogenic signal Sig.sub.kar. An exemplary embodiment of this attenuation function block 21 is described in the subsequently published patent disclosure DE 10 2020 002 572 A1 (corresponding publication US2021338176 (A1) is incorporated herein by reference).

    [0165] FIG. 6 shows in an exemplary manner how a cardiogenic reference signal segment SigA.sub.kar,ref is generated. The time in [sec] is plotted on the x axis, and the signal value in μV is plotted on the y axis. In addition, the initialization phase Ip and the use phase Np are shown. In the example shown, N=9.

    [0166] The following signals are shown in FIG. 6 from top to bottom: [0167] the raw signal Sig.sub.raw, [0168] the sum signal Sig.sub.sum, which the measured value processor 19 generates from the raw signal Sig.sub.raw, [0169] the sequence of the detected heartbeat times H_Zp(x.sub.1), H_Zp(x.sub.N), H_Zp(y.sub.1), H_Zp(y.sub.M), . . . , [0170] the compensation signal Sig.sub.com, which is generated from the cardiogenic reference signal segment SigA.sub.kar,ref by using the detected heartbeat times H_Zp(y.sub.1), H_Zp(y.sub.M), [0171] again the sequence of the detected heartbeat times H_Zp(x.sub.1), H_Zp(x.sub.N), H_Zp(y.sub.1), [0172] the N sum signal segments SigA.sub.Sum(x.sub.1), SigA.sub.Sum(x.sub.N) for the N heartbeat periods H_Zr(x.sub.1), H_Zr(x.sub.N) of the initialization phase Ip, and [0173] schematically how the arithmetic mean is formed over the N sum signal segments SigA.sub.Sum(x.sub.1), SigA.sub.Sum(x.sub.N) and is used as the cardiogenic reference signal segment SigA.sub.kar.ref.

    [0174] It can be seen that the raw signal Sig.sub.raw has low-frequency oscillations, i.e., oscillations with a frequency lower than the heartbeat rate. In addition, the signal values are between −2000 μV and −500 μV. The sum signal Sig.sub.Sum has signal values between 0 μV and 1000 μV and has no low-frequency oscillations.

    [0175] In the subsequent use phase Np, the compensation signal Sigcom is used by using the cardiogenic reference signal segment SigA.sub.kar.ref, and the heartbeat times H_Zp(y.sub.1), H_Zp(y.sub.M), . . . are calculated. As already explained, the cardiogenic reference signal segment SigA.sub.kar.ref is preferably updated continuously, for which the respective last N sum signal segments SigA.sub.Sum(x.sub.1), SigA.sub.Sum(x.sub.N) are used.

    [0176] The upper four signals are generated with a higher sampling frequency and/or in a shorter calculation period. The lower three signals are generated with a lower sampling frequency and/or in a longer calculation period and therefore, as a rule, with higher accuracy.

    [0177] FIG. 7 shows at the top an exemplary course of the compensation signal Sig.sub.com. This exemplary course is formed because the compensation function block 20 processes, as just described, the sum signal Sig.sub.Sum shown as an example in FIG. 5 and FIG. 6. In addition, two exemplary heartbeat time periods H_Zp(y.sub.1) and H_Zp(y.sub.M) are shown in FIG. 7.

    [0178] A reference heartbeat time period H_Zrn, is predefined. The time in the reference heartbeat time period H_Zr.sub.ref is designated by τ. FIG. 7 shows how the same reference heartbeat time period H_Zr.sub.ref is mapped to these two characteristic heartbeat times H_Zp(y.sub.1) and H_Zp(y.sub.M) one after the other. This mapping is necessary, so that the functional unit 11 can carry out the compensation shown in FIG. 5. The respective characteristic heartbeat time H_Zp(y.sub.1), H_Zp(y.sub.M) is needed for this mapping as well.

    [0179] For example, if the estimated respiratory signal Sig.sub.res.est is used for regulation of the ventilator 1, a new signal value is quasi needed in real time. The following problem additionally occurs in this case: The newest segment SigA.sub.kar.syn(x) of the synthetic cardiogenic signal Sig.sub.kar.syn may only be positioned in a correctly timed manner with sufficient accuracy if the characteristic heartbeat time H_Zp(x) has been detected. This is, as a rule, the case only if the R peak of this heartbeat has been detected, however. The newest sum signal segment SigA.sub.Sum(x) cannot be positioned precisely in a correctly timed manner, but only estimated temporally in the period between the beginning of a heartbeat and the R peak of this heartbeat.

    [0180] The process according to the present invention improves the temporal positioning by calculation of a heartbeat, and more precisely, the detection of each characteristic heartbeat time H_Zp(x). This positioning by calculation is also designated as QRS detection.

    [0181] A heartbeat detection in real time is required for some applications. More precisely: A characteristic heartbeat time H_Zp(x) shall already be detected before the heartbeat time period H_Zr(x) of this heartbeat x is ended. Therefore, it is desired to detect the characteristic heartbeat time H_Zp(x) with an accuracy below 0.5 msec, preferably below 0.25 msec.

    [0182] One idea of the process is to analyze the sum signals of different measuring channels, as well as by different methods, as a result of which a plurality of estimations for characteristic heartbeat points are determined for each period. Each estimation for a characteristic heartbeat time is, as a rule, provided with an unavoidable error. The quality of the estimation is evaluated with a quality indicator.

    [0183] FIG. 8 shows one possible application of the present invention. In this application, the ventilator I shall be actuated in order to trigger ventilation strokes. The ventilation strokes shall be carried out in a manner synchronized with the intrinsic breathing activity of the patient P. A control device 18 generates control commands for the ventilator 1 and uses for this an estimation Sigres.est for the respiratory signal Sig.sub.res.

    [0184] In the exemplary embodiment, the control device 18 uses two different estimations Sig.sub.res,est[1] and Sigre,est[2] for the respiratory signal Sig.sub.res. The first estimation Sig.sub.res.est[1] is based on measured values of the intercostal pair 2.1 of measuring electrodes, and the second estimation Sig.sub.res.est[2] is based on measured values of the pair 2.2 of measuring electrodes located near the diaphragm. The intercostal pair of measuring electrodes 2.1 delivers a first sum signal Sig.sub.Sum[1], from which the first estimation Sig.sub.res.est[1] for the respiratory signal Sig.sub.res is derived. The pair of measuring electrodes 2.2 located near the diaphragm delivers a second sum signal Sig.sub.Sum[2], from which the second estimation Sig.sub.res.est[2] for the respiratory signal Sig.sub.res is derived. In practice, these two sum signals Sig.sub.Sum[1] and Sig.sub.Sum[2] differ from one another, among other things because of the different positions of the measuring electrodes 2.1.1 through 2.2.2 on the skin of the patient P and thus because of the different positions in relation to the signal sources (heart muscles and respiratory muscles). Ideally, the cardiac activity acts on both sum signals Sig.sub.Sum[1] and Sig.sub.Sum[2] simultaneously, often with different intensity.

    [0185] The compensation function block 20, which was described with reference to FIG. 5, compensates by calculation the effect of the cardiac activity, i.e., the effect of the cardiogenic signal Sig.sub.kar, especially the effect on the first sum signal Sig.sub.Sum[1], on the one hand, and the effect on the second sum signal Sig.sub.Sum[2], on the other hand. By the compensation function block 20 compensating by calculation the effect of the cardiogenic signal Sig.sub.kar on the first sum signal Sig.sub.Sum[1], the compensation function block 20 calculates the first estimation Sig.sub.res.est[1] for the respiratory signal Sig.sub.res. By the compensation function block 20 compensating by calculation the effect of the cardiogenic signal Sig.sub.kar on the second sum signal Sig.sub.Sum[2], it calculates the first estimation Sig.sub.res.est[2] for the respiratory signal Sig.sub.res. Therefore, the compensation function block 20 is shown schematically twice in FIG. 8.

    [0186] The compensation block 20 uses for the compensation by calculation, in addition, a cardiogenic reference signal segment for the sum signal Sig.sub.Sum[1] and a cardiogenic reference signal segment for the sum signal Sig.sub.Sum[2], i.e., a total of two cardiogenic reference signal segments SigA.sub.kar.ref[1] and SigA.sub.kar.ref[2]. In case three or even more different sum signals are used to derive a respective estimation for the respiratory signal, three or even more cardiogenic reference signal segments are also derived and used. These at least two reference signal segments are stored in the memory 9 and approximately describe the course of the cardiogenic signal Sig.sub.kar in the course of a single heartbeat. These cardiogenic reference signal segments SigA.sub.kar.ref[1] and SigA.sub.kar.ref[2] were preferably generated on the basis of the last N heartbeats, as this was described above. It is also possible to use two respective adapted signal segments SigA.sub.kar.syn[1](x) and SigA.sub.kar.syn[2](x) for each heartbeat.

    [0187] The cardiogenic reference signal segment SigA.sub.kar.ref must be positioned in a correctly timed manner to the first sum signal or the second sum signal. The function block 20 uses for this the respective characteristic heartbeat time H_Zp(x) of a heartbeat. Because the estimations Sig.sub.res.est[1] and Sig.sub.res.est[2] generated by the function block 20 are used to actuate the ventilator 1, estimations in real time are needed for these characteristic heartbeat times. In particular, a characteristic heartbeat time H_Zp(x) is needed before the heartbeat time period H_Zr(x) of this heartbeat has elapsed, i.e., before the heartbeat x is carried out completely.

    [0188] The function block 22 calculates a respective estimation H_Zp[f](x) of the characteristic heartbeat time H_Zp(x) in real time for each heartbeat x. The abbreviation f means “fast.” This estimation H_Zp[f](x) is available sufficiently quickly, so that the function block 20 can approximately position the cardiogenic reference signal segment SigA.sub.kar.ref in a correctly timed manner.

    [0189] In the exemplary embodiment, the function block 22 uses signals from five different measuring channels, namely [0190] the first sum signal Sig.sub.Sum[1], which was generated on the basis of the intercostal pair of measuring electrodes 2.1, [0191] the second sum signal Sig.sub.Sum[2], which was generated on the basis of the pair of measuring electrodes 2.2 located near the diaphragm, [0192] a third sum signal Sig.sub.Sum[3], which was generated on the basis of measured values of the catheter 17 at the cuff 7 for the invasive measurement of the blood pressure, [0193] a fourth sum signal Sig.sub.Sum[4], which was generated on the basis of measured values of the first finger clip 8.1 for the non-invasive measurement of the oxygen saturation, and [0194] a fifth sum signal Sig.sub.Sum[5], which was generated on the basis of measured values of the second finger clip 8.2 for the non-invasive measurement of the blood pressure.

    [0195] It is possible to obtain additional sum signals (not shown) by means of the following sensors: [0196] with mechanomyographic (MMG) sensors, [0197] by means of a process of electrical impedance tomography (EIT), for example, of an EIT belt around the lungs and/or around the chest of the patient P, [0198] by means of the probe 6 in the trachea Sp, which measures the tracheal pressure P.sub.es, [0199] by means of a strain gauge at the chest of the patient P, and [0200] by means of the optical sensor 4, which optimally measures the body of the patient P, cf. FIG. 2.

    [0201] FIG. 8 shows five measured value processors, namely [0202] a measured value processor 23.1 for the intercostal pair of measuring electrodes 2.1, wherein the measured value processor 23.1 delivers the first sum signal Sig.sub.Sum[1], [0203] a measured value processor 23.2 for the pair of measuring electrodes 2.2 located near the diaphragm, wherein the measured value processor 23.2 delivers the second sum signal Sig.sub.Sum[2]. [0204] a measured value processor 23.3 for the cuff 7, wherein the measured value processor 23.3 delivers the third sum signal Sig.sub.Sum[3], [0205] a measured value processor 23.4 for the first sensor 8.1, wherein the first sensor 8.1 measures the oxygen saturation and wherein the measured value processor 23.4 delivers the fourth sum signal Sig.sub.Sum[4], and [0206] a measured value processor for the second sensor 8.2, wherein the second sensor 8.2 measures the oxygen saturation and wherein the measured value processor delivers the fifth sum signal Sig.sub.Sum[5].

    [0207] Ideally, all these five measuring channels 2.1 through 8.2 deliver the same characteristic heartbeat time H_Zp(x) for a heartbeat x, because all sum signals result from a superimposition of the same cardiac activity and the same breathing activity of the patient P. In practice, however, disturbance variables act in different ways on these five sum signals Sig.sub.Sum[1] through Sig.sub.Sum[5], especially because the five sensors 2.1 through 8.2 are arranged at different positions on the skin of the patient P. The sum signals Sig.sub.Sum[3] through Sig.sub.Sum[5] are used in the exemplary embodiment only for the detection of the characteristic heartbeat times, but, moreover, not for the estimation of the respiratory signal Sig.sub.res.

    [0208] The function block 22 applies up to five different detectors 25.1 through 25.5, wherein each detector pertains to a respective sum signal Sig.sub.Sum[1] through Sig.sub.Sum[5] and delivers an estimation (representation) for the actual characteristic heartbeat time H_Zp(x). Such an estimation is often also designated as QRS detection. As is suggested in FIG. 8, an estimation for the characteristic heartbeat time depends considerably on the sensor used. It is possible that a detector 25.1 through 25.5 does not detect individual heartbeats.

    [0209] In one variation, the function block applies two detectors 25.1.f, 25.1.s to the first sum signal Sig.sub.Sum[1] and two additional detectors 25.2.f, 25.2.s to the second sum signal Sig.sub.Sum[2], wherein the two detectors 25.1.f and 25.2.f deliver a respective result (an estimation for a characteristic heartbeat time) H_Zp[1.f](x), H_Zp[2.f](x) in real time and the two other detectors 25.1.s and 25.2.s deliver a result H_Zp[1.s](x), H_Zp[2.s](x) only after the end of the QRS phase, but deliver this result with higher accuracy. In this embodiment as well, the function block 22 may apply at least one of the additional detectors 25.3, 25.4, 25.5.

    [0210] Some sensors, especially non-electrical sensors, inevitably deliver a sum signal with delays. One example is the catheter 17, which is filled with a liquid and is held by the cuff 7. The catheter 17 measures the time course of the blood pressure invasively. The pressure propagates in the liquid in the catheter 17 approximately at the speed of sound. A time delay results from the propagation speed of the pressure in the catheter 17.

    [0211] In one embodiment, the function block 22 uses only results from the two detectors 25.1.f and 25.21 These results pertain to the two sum signals Sig.sub.Sum[1] and Sig.sub.Sum[2], which were generated from measured values of the two pairs of measuring electrodes 2.1 and 2.2, wherein the two sum signals Sig.sub.Sum[1] and Sig.sub.Sum[2] deliver the two estimations H Zp[11](x) and H_Zp[2.f](x) to calculate the estimation H_Zp[f](x) in real time.

    [0212] In addition, FIG. 8 shows five optional functional units 24.1 through 24.5, which each calculate a quality indicator Qu(1) through Qu(5). The respective quality indicator Qu(1) through Qu(5) indicates how well the characteristic heartbeat time can be detected in the respective sum signal Sig.sub.Sum[1] through Sig.sub.Sum[5]. The function block 22 additionally uses these five quality indicators Qu(1) through Qu(5) in one embodiment.

    [0213] As was already explained, each detector 25.1 through 25.5 delivers a respective estimation for the characteristic heartbeat time of a heartbeat. Each functional unit 24.1 through 24.5 delivers a respective quality indicator Qu(1) through Qu(5) for the estimation, which the detector 25.1 through 25.5 calculates. In one embodiment, the function block 22 selects a detection result as a function of the five quality indicators Qu(1) through Qu(5), namely the detection result with the highest quality indicator. It is also possible that the function block 22 calculates a weighted mean of the estimations of the five detectors 25.1 through 25.5. The greater the quality indicator is, the greater is the weighting factor, with which an estimation is included in the weighted mean.

    [0214] Exemplary detectors for characteristic heartbeat times are described in the following publications which are each incorporated herein by reference: [0215] EP 3 600 004 B1 (corresponding to US2020100697 A 1), [0216] L. Kahl, U. G. Hofmann: “Removal of ECG artifacts from EMG signals with different artifact magnitudes by template subtraction,” Current Directions in Biomedical Engineering, 2019, Vol. 5, No. 1, pp. 357-360, [0217] J. Pan, W. J. Tompkins: “A real-time QRS detection algorithm,” IEEE Trans Biomedical Engineering, Vol. 32, No. 3 (1985), pp. 230-236, [0218] A. G. Ramakrishnan, P. Prathosh, T. V. Ananthapadmanabha: “Threshold-independent QRS detection using the dynamic position index,” IEEE Signal Process Lett, Vo. 21, No. 5 (2014), pp. 554-558, and [0219] D. S. Benitez, P. A. Gaydecki, A. Zaidi, A. P. Fitzpatrick: “A new QRS detection algorithm based on the Hilbert transform,” in: Computers in cardiology, 2000, pp. 379-382.

    [0220] Such detectors can also be applied to the present invention.

    [0221] It is possible that at least one detector comprises an optimal filter (matched filter), wherein such an optimal filter looks for a defined pattern in a signal and detects each time period, in which the signal has this pattern. In the present case, this pattern is the typical course of a cardiogenic signal, wherein this typical course reaches from the P peak up to the T peak or even comprises only the QRS phase, cf. FIG. 1.

    [0222] An exemplary detector delivers a signal dependent on the cardiogenic signal and on the respiratory signal, which is used for the detection. Such a signal is, e.g., the signal Gm*** from L. Kahl, U. G. Hofmann, mentioned above. Detected are the intervals, in which this signal is above a first threshold value. Each maximum signal value in such an interval is used as a characteristic heartbeat point. A quality indicator depends on the following criteria: [0223] How far is the maximum signal value in such an interval above the first threshold value? [0224] How far is the signal below the first threshold value outside of such an interval? [0225] In how many intervals and/or in which periods is the signal in a range between the first threshold value and a lower second threshold value? [0226] How high is the signal to noise ratio (SNR)?

    [0227] The quality indicator Qu(l) through Qu(5) depends on the implementation of the respective used detector 25.1 through 25.5. In one embodiment, additional variables are calculated, for example [0228] the EMG to ECG ratio, i.e., the RMS(EMG)/R-S(ECG) ratio, wherein RMS(EMG) is the effective value (root mean square) of a signal of an electromyographic sensor, and R-S(ECG) is the interval between the R peak and the S peak, cf. FIG. 1 and FIG. 7, bottom, [0229] an indicator of the regularity of the heartbeat.

    [0230] As already explained, the two functional units 14 and 15 of the function block 20 continuously update the cardiogenic reference signal segment SigA.sub.kar.ref and store this in the memory 9. The two functional units 14 and 15 use for this the 2*N sum signal segments of the two sum signals Sig.sub.Sum[1] and Sig.sub.Sum[2] for the N heartbeat time periods of the last N heartbeats x.sub.1, . . . , x.sub.N. This is preferably repeated continuously.

    [0231] The two functional units 14 and 15 also require a respective characteristic heartbeat time for these N heartbeats x.sub.1, . . . , X.sub.N. This update of the cardiogenic reference signal segment SigA.sub.kar.ref does not need to take place in real time, however. Therefore, more time is available to detect a heartbeat time approximately. Therefore, the result frequently has a higher reliability. The function block 22 calculates N estimations H Zp[s](x.sub.1), H Zp[s](x.sub.N) for N characteristic heartbeat times H_Zp(x .sub.1), H_Zp(x.sub.N). The s stands for “slow.” In order to calculate these N characteristic estimations H_Zp[s](x.sub.1), H_Zp[s](x.sub.N), the functional units 14 and 15 use the respective N last sum signal segments from the five sum signals Sig.sub.sum[1] through Sig.sub.Sum[5]. In addition, the functional units 14 and 15 apply the five detectors 25.1 through 25.5 to the five sum signals Sig.sub.Sum[1] through Sig.sub.Sum[5], namely a respective detector to a sum signal. Optionally, the function block 22 additionally uses the five quality indicators Qu(1), Qu(5).

    [0232] Each detector 25.1 through 25.5 delivers at least one respective estimation H_Zp[1](x), H_Zp[5](x) for the actual characteristic heartbeat time H_Zp(x) of a heartbeat x. In one embodiment, the two detectors 25.1 and 25.2, which are applied to the sum signals Sig.sub.Sum[1] and Sig.sub.Sum[2] from the two pairs of measuring electrodes 2.1 and 2.2, two respective estimations, namely an estimation H_Zp[1.f](x), H_Zp[2.f](x) in real time and an estimation after a longer computing time H_Zp[1.s](x), H_Zp[2.s](x) and with higher accuracy. The function block 22 combines this estimation, on the one hand, in real time to form an estimation H_Zp[f](x) and, on the other hand, with longer computer time to form an additional estimation H_Zp[s](x).

    [0233] As already explained, each functional unit 24.1, . . . , 24.5 delivers a respective quality indicator Qu(1), Qu(5), cf. FIG. 8. The function block 22 uses the detection results of the detectors 24.1 through 24.5 as well as the quality indicators Qu(1) through Qu(5) in order to calculate at least one estimation H_Zp[f](x), H_Zp[s](x) for the actual characteristic heartbeat time H_Zp(x). In one preferred embodiment, the function block 22 additionally delivers a respective quality indicator for each estimation. The quality indicator for an estimation is based on the quality indicators of the detection results, which the function block 22 uses for the calculation of this estimation ItZp[f](x), H_Zp[s](x). In case, for example, the detection result with the highest quality indicator is used as the estimation, the quality indicator of the estimation is equal to the quality indicator of this detection result.

    [0234] A plurality of detection results can be combined (merged) to form an estimation in different ways. For example, the function block 22 forms a weighted mean value or a weighted median, wherein the weighting factors depend on the quality indicators Qu(1), Qu(n). In another embodiment, the detection result of the detector 25.i, to which the greater quality indicator Qu(2) is assigned, is used.

    [0235] In the example from FIG. 8, the function block delivers, on the one hand, in real time an estimation H_Zp[t](x) for the characteristic heartbeat time H_Zp(x) as well as a quality indicator Qu[f](x) for this estimation H_Zp[f](x). This is carried out for each characteristic heartbeat time H_Zp(x), which at least one detector 25.1 through 25.5 has detected. On the other hand, the function block 22 delivers with higher accuracy and, as a result, with longer computing time a respective estimation H_Zp[s](x.sub.1), H_Zp[s](x.sub.N) for the heartbeat time H_Zp(x.sub.1), H_Zp(x.sub.1) and a respective quality indicator Qu[s](x.sub.1), Qu[s](x.sub.N) for this estimation H_Zp[s](x.sub.1), H_Zp[s](x.sub.N). Each quality indicator Qu[f](x), Qu[s](x) may vary from heartbeat time to heartbeat time. The compensation function block 20 uses these quality indicators Qu[f](x), Qu[s](x) to calculate the estimation Sig.sub.res.est.

    [0236] It is possible that a systematic offset of a detector 25.1 is determined empirically, for example, a systematic time interval between the estimation H Zp[i](x) and the actual characteristic heartbeat time H_Zp(x). This offset may, of course, be determined empirically only if a plurality of heartbeats have elapsed, and is then compensated before the combination (fusion) of the five detection results.

    [0237] The detection results H_Zp[1](x), H_Zp[5](x) or H_Zp[1.s](x), H_Zp[2.s](x), H_Zp[3](x), H_Zp[5](x) of all five detectors 25.1, . . . , 25.5 are preferably combined to determine the estimation H_Zp[s](x) later, for example, only after the QRS phase, and with longer computing time and greater accuracy. Less computing time is available to calculate the estimation H_Zp[f](x) in real time. In one embodiment, the two estimations H_Zp[1.f](x) and H_Zp[2.if](x) are combined. In another embodiment, an iterative process with a stopping criterion is applied to the detection results of the two detectors 25.1 and 25.2 or even the detection results of all five detectors 25.1, . . . , 25.5. As soon as a result with a sufficiently high quality indicator is available, this result is used as the estimation H_Zp[f](x). In case a predefined time limit has elapsed, the last result of the detector obtained with the highest quality indicator or the last obtained result of a fusion is used as the estimation H_Zp[f](x). The time limit depends here on the predefined real-time requirement.

    [0238] Different methods for combining (fusing) detection results are described in the following reference, which is incorporated herein by reference: C. A. Ledezma, M. Altuve: “Optimal data fusion for the improvement of QRS detection in multi-channel ECG recordings,” Medical & Biological Engineering & Computing, Vol. 57 (2019), pp. 1673-1681. These methods can in some cases also be applied as embodiments of the present invention.

    [0239] FIG. 9 illustrates, for example, an embodiment of the present invention. The two sum signals Sig.sub.Sum[1] and Sig.sub.Sum[2], which are obtained by processing measured values of the two pairs of measuring electrodes 2.1 and 2.2, are used in this example. These two signals are shown as examples. The two detectors 25.1.f and 25.1s deliver a respective estimation H_Zp[1.f](x.sub.1), H_Zp[1.f](x2), . . . in real time and an estimation H_Zp[l.s](x.sub.1), H_Zp[1.s](x.sub.2), . . . with longer computer time, especially both times for the heartbeats x.sub.1, x.sub.2, . . . and based on the same sum signal Sig.sub.Sum[1]. The two detectors 25.2.f and 25.2.s deliver an estimation H_Zp[2.f](x.sub.1), H_Zp[2.f](x.sub.2), . . . in real time and an estimation H_Zp[2.s](x.sub.1), H_Zp[2.s](x.sub.2), . . . with longer computing time, especially both times for the heartbeats x.sub.1, x.sub.2, . . . and based on the same sum signal Sig.sub.Sum[2]. The two detectors 25.1.f and 25.2.f are designated as real time detectors below, the two detectors 25.1.s and 25.2.s are designated as more accurate detectors.

    [0240] On the one hand, an estimation H_Zp[f](x.sub.1), H_Zp[f](x.sub.2), . . . is calculated and used, for example, for the control of the ventilator 1. For this, the estimations H_Zp[1.f](x.sub.1), H_Zp[1.f](x.sub.2), . . . , as well as H_Zp[2.f](x.sub.1), H_Zp[2.f](x.sub.2), . . . of the two real time detectors 25.1.f and 25.2.f are used, and optionally estimations of such detectors, which analyze other sum signals, but not the estimations H_Zp[1.s](x.sub.1), H_Zp[1.s](x.sub.2), . . . and H_Zp[2.s](x.sub.1), H_Zp[2.s](x.sub.2), . . . of the more accurate detectors 25.1.s and 25.1.s.

    [0241] On the other hand, four detection results are combined, which requires, as a result, a longer computing time. This combination (fusion) is therefore used exclusively for calculating the estimation H_Zp[s](x.sub.1), H_Zp[s](x.sub.2), . . . How this happens is described below in an exemplary manner.

    [0242] Four diagrams with three respective actual heartbeat times and the respective estimated heartbeat times are shown as examples. The time is plotted on the x axis. The characteristic heartbeat times H_Zp(x.sub.1), H_Zp(x.sub.2), H_Zp(x.sub.3), which are actual and are not known in practice, are indicated by vertical broken lines. The respective sequence of the raw estimations H_Zp_r[1.f](x.sub.1), H_Zp_r[1.f](x.sub.2), H_Zp[2.s](x.sub.1), H_Zp[2.s](x.sub.2), . . . of the heartbeat times, which the four detectors 25.1.f and 25.1.s as well as 25.2.f and 25.2.s have detected, are shown by vertical solid lines with constant height, for example, the height 1 (Dirac pulse). Note: The detector 25.2.f operating in real time has not detected the second heartbeat time H_Zp(x.sub.2).

    [0243] The respective raw estimation H_Zp_r[1.f](x), H_Zp[2.s](x) for the characteristic heartbeat time deviates from the actual characteristic heartbeat time H_Zp(x). For a number of N retrograde heartbeats, it is automatically determined how great this deviation between the raw estimation and the actual time is (sign and value of the deviation). Instead of the actual heartbeat time, the respective estimation for the characteristic heartbeat time is used, which was obtained by fusion of the different estimations. An estimation for the probability distribution of the deviation is derived with a statistical method, for example, with a histogram. Four such estimated probability distributions (more precisely: derived convolution kernels) Wv(25.1f), Wv(25.2.s) for the four detectors are suggested in FIG. 9. It can be seen that the detectors 25.1.s and 25.2.s, which are operating with longer computing time, have a more narrow estimation for the probability, i.e., less scattering than the two detectors 25.1.f and 25.2.f operating in real time. These estimations are preferably updated continuously for the four probability distributions, namely on the basis of the last respective N heartbeats.

    [0244] The estimated probability distribution Wv(25.1.f), . . . Wv(25.2.s) for a detector 25.1.f, . . . , 25.s.2 is combined with the sequence of estimations H_Zp[1.f](x.sub.1), H_Zp[1.f](x.sub.2), H_Zp[2.s](x.sub.1), H_Zp[2.s](x.sub.2), . . . for the heartbeat times, which this detector delivers. For example, a convolution is applied for the combination. Due to the combination, the effect of a systematic error of a detector 25.1.f, . . . , 25.s.2 is compensated by calculation. The estimation for a heartbeat time is then usually no longer a precise time, but rather is reproduced by a kind of distribution. The compensation delivers a respective compensated heartbeat time H_Zp_k[1.f](x.sub.1), H_Zp_k[1.f](x.sub.1), H_Zp_k[1.f](x.sub.2), H_Zp_k[2.s](x.sub.1), H_Zp_k[2.s](x.sub.2),

    [0245] The four signals H_Zp_k[1.f](x.sub.1), H_Zp_k[1.f](x.sub.2), H_Zp_k[2.s](x.sub.1), H_Zp_k[2.s](x2), . . . of the compensated heartbeat times are subsequently attenuated with a weighting factor α(1.f), α(1.s), α(2f), α(2.s). Preferably, α(1.f)+α(1.s)+α(2.f)+α(2.s)=1. In addition, a vertical offset off_v(1.f), off_v(1.s), off_v(2.f), off_v(2.s), i.e., a constant increase or reduction of the respective signal value, is applied. This vertical offset off_v(1.f), off_v(1.s), off_v(2.f), off_v(2.s) takes into consideration the fact that the four detectors 25.1.f, 25.1.s, 25.2.f, 25.2.s deliver detection results of different quality and can be tared differently between specificity (detect no incorrect time) and sensitivity (detect each correct time).

    [0246] Four signals H_Zp[1.f](x.sub.1), H_Zp[1.f](x.sub.2), . . . , H_Zp[2.s](x.sub.1), H_Zp[2.s](x.sub.2) are calculated by applying these four weighting factors a(1.0, a(1 .$), α(2.f), α(2.s) and these four offsets off_v(1.f), off_v(1.s), off_v(2.f), off_v(2.s). These four signals are added. In this signal, which is formed by addition, each maximum value is sought, which is above a predefined limit. The time corresponding to this signal value is then a heartbeat time H_Zp(x.sub.1), H_Zp(x.sub.2),

    [0247] FIG. 10 shows how a respective heartbeat time H_Zp[f](x.sub.1), H_Zp[f](x.sub.2), . . . is detected by means of detection results H_Zp[1.f](x.sub.1), H_Zp[1.f](x.sub.2), H_Zp[1.s](x.sub.1), H_Zp[1.s](x.sub.2), of the two real time detectors 25.1.f and 25.2.f in an exemplary manner. Identical reference numbers have the same meanings as in FIG. 7. The two offsets off_h(1) and off_h(2) are horizonal offsets, i.e., time shifts, and are calculated as a function of the two estimated probability distributions Wv(25.1.f), . . . , Wv(25.2.f). A respective systematic error of a real time detector 25.1, 25.2 is compensated by calculation in this manner. The estimated probability distributions Wv(25.1.f), . . . , Wv(25.2.f) are calculated as was described with reference to FIG. 9.

    [0248] While specific embodiments of the invention have been shown and described in detail to illustrate the application of the principles of the invention, it will be understood that the invention may be embodied otherwise without departing from such principles.

    TABLE-US-00001 List of Reference Characters  1 Ventilator; it mechanically ventilates (ventilates) and monitors the patient P; it comprises the signal processing unit 5  2.1 Intercostal (located near the heart) pair of measuring electrodes; it comprises the measuring electrodes 2.1.1 and 2.1.2; it delivers measured values for the electrical sum signal Sig.sub.Sum[1] 2.1.1, 2.1.2 Measuring electrodes of the intercostal pair 2.1  2.1 Pair of measuring electrodes located near the diaphragm; it comprises the measuring electrodes 2.2.1 and 2.2.2; it delivers measured values for the electrical sum signal Sig.sub.Sum[2] 2.2.1, 2.2.2 Measuring electrodes of the pair located near the diaphragm 2.2  3 Pneumatic sensor in front of the mouth of the patient; it measures the volume flow Vol' and the airway pressure P.sub.aw  4 Optical sensor with an image recording device and with an image processing unit; it measures the geometry of the body of the patient P, from which the current lung filling level Vol is derived by calculation  5 Signal processing unit; it comprises the function blocks 20 and 21; it carries out the steps of the process according to the present invention; it has reading access and writing access to the memory 9  6 Probe in the trachea Sp; it measures the pneumatic pressure P.sub.es in the trachea Sp  7 Cuff around a wrist of the patient P; it holds the catheter 17, which invasively measures the time course of the blood pressure  8.1 Sensor in the form of a finger clip on one finger of the patient P; it measures the degree of saturation of the blood with oxygen non-invasively  8.2 Sensor in the form of a finger clip on another finger of the patient P; it non- invasively measures the blood pressure of the patient P  9 Memory, to which the signal processing unit 5 has reading access and writing access and in which the cardiogenic reference signal segment SigA.sub.kar, ref is stored 10 Functional unit of the compensation function block 20; it generates the synthetic cardiogenic signal Sig.sub.kar, syn 11 Functional unit of the compensation function block 20; by using the synthetic cardiogenic signal Sig.sub.kar, syn, it compensates the effect of the cardiogenic signal Sig.sub.kar on the sum signal Sig.sub.Sum, for example, by subtraction of Sig.sub.kar, syn 12 Functional unit of the signal processing unit 5; it detects the respective QRS interval of each heartbeat in the sum signal Sig.sub.Sum 13 Functional unit of the signal processing unit 5; it detects the exact heartbeat time H_Zp(n) of each heartbeat 14 Functional unit of the compensation function block 20; it superimposes the sum signal segments for a respective heartbeat by calculation 15 Functional unit of the compensation function block 20; it generates a cardiogenic reference signal segment SigA.sub.kar, ref 16 Functional unit of the compensation function block 20; it positions the cardiogenic reference signal segments SigA.sub.kar, ref in a correctly timed manner as a function of the heartbeat time H_Zp(x); it combines the positioned cardiogenic reference signal segments SigA.sub.kar, ref to form the synthetic cardiogenic signal Sig.sub.kar, syn 17 Catheter, held by the cuff 7; it measures the time course of the blood pressure of the patient P invasively 18 Control device, which actuates the ventilator 1 and generates for this control signals comm as a function of estimated respiratory signals 19 Measured value processor; it generates the sum signal Sig.sub.Sum from reinforced measured values of the sensors 2.1.1 through 2.2.2 20 Compensation function block; it generates the synthetic cardiogenic signal Sig.sub.kar, syn and the compensation signal Sig.sub.com 21 Attenuation function block; it generates the estimated respiratory signal Sig.sub.res, est by attenuation from the compensation signal Sig.sub.com 22 Function block, which determines the respective characteristic heartbeat time H_Zp(x) of each heartbeat; it comprises the functional units 12 and 13 23.1 Measured value processor for the intercostal pair of measuring electrodes 2.1; it sends the first sum signal Sig.sub.Sum[1] 23.2 Measured value processor for the pair of measuring electrodes 2.2 located near the diaphragm; it sends the second sum signal Sig.sub.Sum(2) 23.3 Measured value processor for the cuff 7; it sends the third sum signal Sig.sub.Sum[3] 23.4 Measured value processor for the first sensor 8.1 for the oxygen saturation; it sends the fourth sum signal Sig.sub.Sum[4] 23.5 Measured value processor for the second sensor 8.2 for the oxygen saturation; it sends the fifth sum signal Sig.sub.Sum[5] 24.1 Functional unit, which calculates the quality indicator Qu(1) 25.1 Detector; it detects an estimation H_Zp[1](x) for the heartbeat time H_Zp(x) by analysis of the first sum signal Sig.sub.Sum[1] 25.1.f Detector; it detects an estimation H_Zp[1.f](x) for the heartbeat time H_Zp(x) by analysis of the first sum signal Sig.sub.Sum[1] in real time 25.1.s Detector; it detects an estimation H_Zp[1.s](x) for the heartbeat time H_Zp(x) by analysis of the first sum signal Sig.sub.Sum[1] with longer computing time comm Control signals for actuating the ventilator 1; they are generated by the control device 18 H_Zp(x) Characteristic heartbeat time of the heartbeat x, detected approximately by the function block 22 H_Zp[1](x) Estimation detected by the detector 25.1 for the characteristic heartbeat time H_Zp(x), detected by analysis of the first sum signal Sig.sub.Sum[1] H_Zp[1.f](x) Estimation detected in real time by the detector 25.1.f for the characteristic heartbeat time H_Zp(x), detected by analysis of the first sum signal Sig.sub.Sum[1] H_Zp_k[1.f](x) Improvement of the estimation H_Zp_r[1.f](x) for the characteristic heartbeat time H_Zp(x), which [improvement] was generated by compensation of the systematic error of the detector 25.1.f by calculation H_Zp_r[1.f](x) Raw estimation of the detector 25.1.f for the characteristic heartbeat time H_Zp(x) H_Zp[1.s](x) Estimation detected with longer computing time by the detector 25.1.s for the characteristic heartbeat time H_Zp(x), detected by analysis of the first sum signal Sig.sub.Sum[1] H_Zp[f](x) Estimation calculated in real time for the characteristic heartbeat time H_Zp(x) of the heartbeat x, calculated by the function block 22 H_Zp[s](x) Estimation calculated with higher accuracy and longer computing time for the characteristic heartbeat time H_Zp(x) of the heartbeat x, calculated by the function block 22 H_Zp[2.f](x) Estimation detected in real time by the detector 25.2.f for the characteristic heartbeat time H_Zp(x), detected by analysis of the second sum signal Sig.sub.Sum[2] H_Zp[2.s](x) Estimation detected with longer computing time by the detector 25.2.s for the characteristic heartbeat time H_Zp(x), detected by analysis of the second sum signal Sig.sub.Sum[2] H_Zp.sub.ref Reference heartbeat time H_Zr(x) Heartbeat time period of the heartbeat x, detected by the functional unit 12 H_Zr.sub.ref Reference heartbeat time period; overlapped by the cardiogenic reference signal segment SigA.sub.kar, ref Ip Initialization phase; it comprises N consecutive heartbeat time periods H_Zr(x1), . . . , H_Zr(xN) N Number of heartbeat time periods of the initialization phase Ip Np Use phase, in which the cardiogenic reference signal segment SigA.sub.kar, ref and the detected heartbeat times H_Zp(y1), . . . are used to generate the compensation signal Sig.sub.com Qu(1) Quality indicator, which indicates with what quality the detector 24.1 detects an actual heartbeat time in the first sum signal Sig.sub.Sum[1] Qu(2) Quality indicator, which indicates with what quality the detector 24.2 detects an actual heartbeat time in the first sum signal Sig.sub.Sum[2] Qu(3) Quality indicator, which indicates with what quality the detector 24.3 detects an actual heartbeat time in the first sum signal Sig.sub.Sum[3] Qu(4) Quality indicator, which indicates with what quality the detector 24.4 detects an actual heartbeat time in the first sum signal Sig.sub.Sum[4] Qu(5) Quality indicator, which indicates with what quality the detector 24.5 detects an actual heartbeat time in the first sum signal Sig.sub.Sum[5] Qu[f](x) Quality indicator for the real time estimation H_Zp[f](x) for the heartbeat time H_Zp(x) Qu[s](x) Quality indicator for the further estimation H_Zp[s](x) for the heartbeat time H_Zp(x) Sig.sub.com Compensation signal; it is generated by the compensation function block 20 by compensation of the contribution of the synthetic cardiogenic signal Sig.sub.kar, ayn to the sum signal Sig.sub.Sum Sig.sub.com(x) Signal segment of the compensation signal Sig.sub.com for the heartbeat x Sig.sub.kar Cardiogenic signal; it brings about the cardiac activity of the patient P, it is estimated by the synthetic cardiogenic signal Sig.sub.kar, syn SigA.sub.kar, ref Cardiogenic reference signal segment; it describes approximately the course of the cardiogenic signal Sig.sub.kar during a single heartbeat; it refers to the reference heartbeat time period H_Zr.sub.ref SigA.sub.kar, ref[1] Cardiogenic reference signal segment, which was obtained from the sum signal Sig.sub.Sum[1] SigA.sub.kar, ref[2] Cardiogenic reference signal segment, which was obtained from the sum signal Sig.sub.Sum[2] SigA.sub.kar, syn(x) Synthetic cardiogenic signal segment for the heartbeat x, generated from the cardiogenic reference signal segment SigA.sub.kar, ref by using a value of an anthropological parameter, which [value] was measured during the heartbeat x Sig.sub.kar, syn Synthetic cardiogenic signal; it is an estimation for the cardiogenic signal Sig.sub.kar; it is generated by the functional unit 10 from the signal segments SigA.sub.kar, syn(x) SigA.sub.kar, syn(x) Segment of the synthetic cardiogenic signal .sub.Sigkar, syn for the heartbeat x Sig.sub.raw Raw signal from the sensors 2.1.1 through 2.2.2 Sig.sub.res Respiratory signal to be determined; it correlates with the intrinsic breathing activity of the patient P, i.e., the breathing activity brought about by the muscles of the diaphragm Sig.sub.res, est Estimation for the respiratory signal Sig.sub.res to be determined Sig.sub.res, est[1] Estimation for the respiratory signal Sig.sub.res to be determined on the basis of the sum signal, which originates from the intercostal pair of electrodes 2.1 Sig.sub.res, est[2] Estimation for the respiratory signal Sig.sub.rcs to be determined on the basis of the sum signal, which originates from the pair of electrodes 2.2 located near the diaphragm Sig.sub.Sum Sum signal, measured by the sum signal sensors 2.1, 2.2, 3 or 4; it results from a superimposition of the respiratory signal Sig.sub.res and of the cardiogenic signal Sig.sub.kar SigA.sub.Sum(x) Sum signal segment for the heartbeat x, generated from the sum signal Sig.sub.Sum; it refers to the reference heartbeat time period H_Zr.sub.ref Sig.sub.Sum[1] Sum signal, which was generated from measured values of the intercostal pair 2.1 Sig.sub.Sum[2] Sum signal, which was generated from measured values of the pair 2.2 located near the diaphragm Sig.sub.Sum[3] Sum signal, which was generated from measured values of the cuff 7 for the blood pressure Sig.sub.Sum[4] Sum signal, which was generated from measured values for the first sensor 8.1 for the oxygen saturation Sig.sub.Sum[5] Sum signal, which was generated from measured values for the second sensor 8.2 for the oxygen saturation Sp Trachea of the patient P Wv(25.1.f) Estimated probability distributions (convolution kernels) for the estimations of the heartbeat time, which the detector 25.1.f delivers